The model is parameterized as a standard feed-forward neural network language model [1]} with an additional contextual encoder \(\text{Enc}\) .
The model parameters are \(\theta = (\mathbf {E}, \mathbf {U}, \mathbf {V}, \mathbf {W})\)  where \(\mathbf {E} \in \mathbb {R}^{D \times V}\)  is a word embedding matrix, \(\mathbf {U} \in \mathbb {R}^{(CD) \times H}\) , \(\mathbf {V} \in \mathbb {R}^{V \times H}\) , \(\mathbf {W} \in \mathbb {R}^{V \times H}\)  are weight matrices, \(4 \times D\)  is the size of the word embeddings, and \(\mathbf {h}\)  is a hidden layer of size \(H\) .
The contextual encoder Enc returns a vector of size \(H\)  representing \((\mathbf {Y}_{C}, \mathcal {D})\) . Enc can be implemented using any sentence encoders developed for extractive summarizers as described in Section REF . [2]} shows that an attention-based encoder works the best.
